Publication

Predicting Mortality in Children With Pediatric Acute Respiratory Distress Syndrome: A Pediatric Acute Respiratory Distress Syndrome Incidence and Epidemiology Study

Pediatric Acute Respiratory Distress Syndrome Incidence and Epidemiology (PARDIE) V1 Investigators and the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network, Yehya, N., Harhay, M. O., Klein, M. J., Shein, S. L., Piñeres-Olave, B. E., Izquierdo, L., Sapru, A., Emeriaud, G., Spinella, P. C., Flori, H. R., Dahmer, M. K., Maddux, A. B., Lopez-Fernandez, Y. M., Haileselassie, B., Hsing, D. D., Chima, R. S., Hassinger, A. B., Valentine, S. L., Rowan, C. M., Kneyber, M. C. J., Smith, L. S., Khemani, R. G. & Thomas, N. J., 8-Apr-2020, In : Critical Care Medicine. 9 p.

Research output: Contribution to journalArticleAcademicpeer-review

  • Pediatric Acute Respiratory Distress Syndrome Incidence and Epidemiology (PARDIE) V1 Investigators and the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network
  • Nadir Yehya
  • Michael O Harhay
  • Margaret J Klein
  • Steven L Shein
  • Byron E Piñeres-Olave
  • Ledys Izquierdo
  • Anil Sapru
  • Guillaume Emeriaud
  • Philip C Spinella
  • Heidi R Flori
  • Mary K Dahmer
  • Aline B Maddux
  • Yolanda M Lopez-Fernandez
  • Bereketeab Haileselassie
  • Deyin Doreen Hsing
  • Ranjit S Chima
  • Amanda B Hassinger
  • Stacey L Valentine
  • Courtney M Rowan
  • Martin C J Kneyber
  • Lincoln S Smith
  • Robinder G Khemani
  • Neal J Thomas

OBJECTIVES: Pediatric acute respiratory distress syndrome is heterogeneous, with a paucity of risk stratification tools to assist with trial design. We aimed to develop and validate mortality prediction models for patients with pediatric acute respiratory distress syndrome.

DESIGN: Leveraging additional data collection from a preplanned ancillary study (Version 1) of the multinational Pediatric Acute Respiratory Distress syndrome Incidence and Epidemiology study, we identified predictors of mortality. Separate models were built for the entire Version 1 cohort, for the cohort excluding neurologic deaths, for intubated subjects, and for intubated subjects excluding neurologic deaths. Models were externally validated in a cohort of intubated pediatric acute respiratory distress syndrome patients from the Children's Hospital of Philadelphia.

SETTING: The derivation cohort represented 100 centers worldwide; the validation cohort was from Children's Hospital of Philadelphia.

PATIENTS: There were 624 and 640 subjects in the derivation and validation cohorts, respectively.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: The model for the full cohort included immunocompromised status, Pediatric Logistic Organ Dysfunction 2 score, day 0 vasopressor-inotrope score and fluid balance, and PaO2/FIO2 6 hours after pediatric acute respiratory distress syndrome onset. This model had good discrimination (area under the receiver operating characteristic curve 0.82), calibration, and internal validation. Models excluding neurologic deaths, for intubated subjects, and for intubated subjects excluding neurologic deaths also demonstrated good discrimination (all area under the receiver operating characteristic curve ≥ 0.84) and calibration. In the validation cohort, models for intubated pediatric acute respiratory distress syndrome (including and excluding neurologic deaths) had excellent discrimination (both area under the receiver operating characteristic curve ≥ 0.85), but poor calibration. After revision, the model for all intubated subjects remained miscalibrated, whereas the model excluding neurologic deaths showed perfect calibration. Mortality models also stratified ventilator-free days at 28 days in both derivation and validation cohorts.

CONCLUSIONS: We describe predictive models for mortality in pediatric acute respiratory distress syndrome using readily available variables from day 0 of pediatric acute respiratory distress syndrome which outperform severity of illness scores and which demonstrate utility for composite outcomes such as ventilator-free days. Models can assist with risk stratification for clinical trials.

Original languageEnglish
Number of pages9
JournalCritical Care Medicine
Publication statusE-pub ahead of print - 8-Apr-2020

ID: 123830096